70% of AI and machine learning projects never make it to production. The gap between prototype and deployment is where most teams fail.
Trusted by 150+ Enterprise Development Teams
★★★★★
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Microsoft Gold Partner
Enterprise Python Development
What You Can Build With Python Developers
Hire Python developers to solve your most complex data and intelligence challenges. These are systems where a single bug in your ML pipeline can corrupt months of training data, where API latency directly impacts revenue, where compliance violations carry seven figure penalties. Our developers integrate with your existing team to deliver production systems that scale, perform, and pass audits.
Machine Learning and AI Systems
Build production ML systems where model performance directly impacts business outcomes. Your current approach probably works in notebooks but breaks when real data arrives with missing values, outliers, and distribution shifts your training set never saw. We build ML pipelines that handle data drift detection, automated retraining triggers, and model versioning. Every model ships with monitoring. Your recommendation engine delivers personalized results in under 100ms while processing user behavior signals in real time. Like the e-commerce client who increased conversion rates 34% after we rebuilt their product recommendation system from prototype to production grade.
Tech Stack:
Outcome:
Sub 100ms inference | 99.9% model uptime | Automated retraining pipelinesData Engineering Pipelines
Build ETL pipelines that process terabytes of data without manual intervention. Your analysts are probably waiting hours for queries that should take minutes because nobody optimized the underlying data architecture. We build pipelines with incremental processing, proper partitioning strategies, and data quality gates that catch issues before they propagate downstream. Short sentences matter here. Bad data in, bad decisions out. Your data warehouse becomes a reliable source of truth instead of a collection of conflicting numbers that nobody trusts.
Tech Stack:
Outcome:
10x faster queries | Zero data quality incidents | Automated lineage trackingBackend APIs and Microservices
Build APIs that handle thousands of concurrent requests without falling over. Your monolith is probably creating deployment bottlenecks that cost you releases every sprint. We decompose services thoughtfully, implement proper circuit breakers, and design APIs that your frontend team actually wants to use. FastAPI gives you automatic OpenAPI documentation and type validation. Django handles the heavy lifting when you need admin interfaces and complex business logic. Your API response times drop from seconds to milliseconds.
Tech Stack:
Outcome:
Sub 50ms p99 latency | 99.99% uptime | Auto generated API documentationAutomation and DevOps
Build automation that eliminates the manual tasks consuming your engineering hours. Your team is probably spending 20% of their time on repetitive work that could be scripted. We build deployment pipelines, infrastructure automation, and testing frameworks that catch bugs before production. Ansible playbooks that actually work across environments. Terraform modules that follow security best practices. Your deployment frequency increases from monthly to daily while incident rates drop.
Tech Stack:
Outcome:
80% reduction in manual tasks | Daily deployments | Zero configuration driftCloud Native Applications
Build applications designed for cloud scale from day one. Your current architecture probably requires manual scaling and costs three times what it should because nobody optimized resource utilization. We build serverless functions that scale to zero when idle and handle traffic spikes without intervention. Container orchestration that self heals. Cost monitoring that catches runaway spending before it hits your invoice. AWS Lambda functions that cold start in under a second.
Tech Stack:
Outcome:
60% infrastructure cost reduction | Auto scaling | Zero manual interventionScientific Computing and Research
Build computational systems that turn research into reproducible results. Your scientists are probably running experiments that nobody can replicate because the environment setup lives only in their heads. We build research platforms with proper dependency management, experiment tracking, and result versioning. Jupyter notebooks become production code. Simulations that took days now complete in hours with proper parallelization. Every experiment is reproducible.
Tech Stack:
Outcome:
100% reproducible experiments | 10x faster simulations | Version controlled researchFintech Applications
Build financial systems where accuracy and compliance are not negotiable. A single calculation error can trigger regulatory scrutiny and erode customer trust. We build trading systems with proper decimal handling, audit trails that satisfy regulators, and risk models that actually predict market conditions. Python powers quantitative analysis at firms from startups to Goldman Sachs. Your compliance team stops dreading audits because every transaction is traceable.
Tech Stack:
Outcome:
Zero calculation errors | Complete audit trails | Real time risk monitoringHealthcare Systems
Build healthcare applications where HIPAA compliance is built in, not bolted on. Patient data breaches carry penalties up to $1.5 million per violation category. We build systems with proper encryption at rest and in transit, audit logging that satisfies OCR investigations, and access controls that enforce least privilege. ML models that assist diagnosis while maintaining explainability requirements. Your legal team reviews our architecture before we write code
Tech Stack:
Outcome:
HIPAA compliant architecture | Complete audit logging | Explainable AI modelsBuild production ML systems where model performance directly impacts business outcomes. Your current approach probably works in notebooks but breaks when real data arrives with missing values, outliers, and distribution shifts your training set never saw. We build ML pipelines that handle data drift detection, automated retraining triggers, and model versioning. Every model ships with monitoring. Your recommendation engine delivers personalized results in under 100ms while processing user behavior signals in real time. Like the e-commerce client who increased conversion rates 34% after we rebuilt their product recommendation system from prototype to production grade.
Tech Stack:
Outcome:
Sub 100ms inference | 99.9% model uptime | Automated retraining pipelinesBuild ETL pipelines that process terabytes of data without manual intervention. Your analysts are probably waiting hours for queries that should take minutes because nobody optimized the underlying data architecture. We build pipelines with incremental processing, proper partitioning strategies, and data quality gates that catch issues before they propagate downstream. Short sentences matter here. Bad data in, bad decisions out. Your data warehouse becomes a reliable source of truth instead of a collection of conflicting numbers that nobody trusts.
Tech Stack:
Outcome:
10x faster queries | Zero data quality incidents | Automated lineage trackingBuild APIs that handle thousands of concurrent requests without falling over. Your monolith is probably creating deployment bottlenecks that cost you releases every sprint. We decompose services thoughtfully, implement proper circuit breakers, and design APIs that your frontend team actually wants to use. FastAPI gives you automatic OpenAPI documentation and type validation. Django handles the heavy lifting when you need admin interfaces and complex business logic. Your API response times drop from seconds to milliseconds.
Tech Stack:
Outcome:
Sub 50ms p99 latency | 99.99% uptime | Auto generated API documentationBuild automation that eliminates the manual tasks consuming your engineering hours. Your team is probably spending 20% of their time on repetitive work that could be scripted. We build deployment pipelines, infrastructure automation, and testing frameworks that catch bugs before production. Ansible playbooks that actually work across environments. Terraform modules that follow security best practices. Your deployment frequency increases from monthly to daily while incident rates drop.
Tech Stack:
Outcome:
80% reduction in manual tasks | Daily deployments | Zero configuration driftBuild applications designed for cloud scale from day one. Your current architecture probably requires manual scaling and costs three times what it should because nobody optimized resource utilization. We build serverless functions that scale to zero when idle and handle traffic spikes without intervention. Container orchestration that self heals. Cost monitoring that catches runaway spending before it hits your invoice. AWS Lambda functions that cold start in under a second.
Tech Stack:
Outcome:
60% infrastructure cost reduction | Auto scaling | Zero manual interventionBuild computational systems that turn research into reproducible results. Your scientists are probably running experiments that nobody can replicate because the environment setup lives only in their heads. We build research platforms with proper dependency management, experiment tracking, and result versioning. Jupyter notebooks become production code. Simulations that took days now complete in hours with proper parallelization. Every experiment is reproducible.
Tech Stack:
Outcome:
100% reproducible experiments | 10x faster simulations | Version controlled researchBuild financial systems where accuracy and compliance are not negotiable. A single calculation error can trigger regulatory scrutiny and erode customer trust. We build trading systems with proper decimal handling, audit trails that satisfy regulators, and risk models that actually predict market conditions. Python powers quantitative analysis at firms from startups to Goldman Sachs. Your compliance team stops dreading audits because every transaction is traceable.
Tech Stack:
Outcome:
Zero calculation errors | Complete audit trails | Real time risk monitoringBuild healthcare applications where HIPAA compliance is built in, not bolted on. Patient data breaches carry penalties up to $1.5 million per violation category. We build systems with proper encryption at rest and in transit, audit logging that satisfies OCR investigations, and access controls that enforce least privilege. ML models that assist diagnosis while maintaining explainability requirements. Your legal team reviews our architecture before we write code
Tech Stack:
Outcome:
HIPAA compliant architecture | Complete audit logging | Explainable AI modelsDO YOU KNOW
Python is used by companies like Google, Netflix, and NASA, powering systems for billions of users worldwide and supported by 15M+ developers across AI, data science, and web development.
Python Developer Survey 2024
Developer Capabilities
Technical Expertise Our Python Developers Bring
Our Python developers average 7.2 years of production experience. Production Python deployed in at least two domains: machine learning, data engineering, backend APIs, or cloud infrastructure. Every developer is vetted for system design thinking and debugging under pressure, not just syntax proficiency.
Core Python Mastery
Python fundamentals matter more than framework knowledge. A developer who understands generators, context managers, and memory management writes code that scales. Our developers use type hints consistently because catching bugs at write time costs less than catching them in production. They understand the Global Interpreter Lock and know when to use multiprocessing versus asyncio. This is not negotiable. Your codebase stays maintainable as it grows from 10,000 to 100,000 lines.
Web Framework Expertise
Framework choice depends on your specific requirements, not developer preference. Django 5.2 LTS provides batteries included development with built in ORM, admin, and authentication that enterprises need. FastAPI 0.115 delivers async first APIs with automatic OpenAPI documentation for high performance microservices. Flask handles lightweight services where Django overhead does not make sense. Our developers know when to use each.
Data Science and ML Libraries
ML library expertise goes beyond importing TensorFlow. Our developers understand when PyTorch's dynamic computation graph makes sense versus TensorFlow's production deployment story. They build feature pipelines with proper validation, train models with appropriate regularization, and deploy with monitoring that catches model degradation. Data preprocessing with Pandas at scale requires chunking strategies and memory management
Cloud Platform Integration
Cloud expertise means knowing AWS, GCP, and Azure SDKs well enough to avoid vendor lock in where it matters. Our developers build Lambda functions with proper cold start optimization, SageMaker pipelines with cost efficient training jobs, and multi cloud architectures when business requirements demand it. They understand IAM policies, VPC configurations, and security groups without hand holding.
Database Technologies
Database choice impacts everything from query performance to operational complexity. Our developers optimize PostgreSQL queries with proper indexing strategies, implement Redis caching that actually improves performance, and design MongoDB schemas that do not require rewrites when requirements change. SQLAlchemy 2.0 for ORM work with understanding of when to drop to raw SQL.
Testing and Quality Assurance
Testing is not optional. Our developers write tests before code for critical paths, use pytest fixtures to avoid test pollution, and maintain coverage above 85% without gaming metrics. They understand property based testing with Hypothesis for edge case discovery, integration testing strategies that do not require full environment setup, and load testing with realistic traffic patterns.
DevOps and CI/CD
DevOps skills separate developers who ship from developers who commit. Our developers containerize Python applications with proper multi stage builds, write GitHub Actions workflows that catch issues before merge, and configure Kubernetes deployments with appropriate resource limits. They understand infrastructure as code with Terraform, monitoring with Prometheus and Grafana, and incident response procedures.
Platform Trajectory
Python Platform Evolution: Why It Matters for Your Project
Python is not just another scripting language. It has evolved into the dominant platform for AI, data science, and backend development. Understanding where Python fits in the technology landscape helps you make informed architecture decisions. Here is how the platform has matured.
Python 2 End of Life
Legacy (No Support)Python 2 reached end of life with no more security patches. This matters because 37% of companies still had majority Python 2 code at EOL. Organizations running Python 2 in production face security vulnerabilities with no official fixes. Migration to Python 3 requires codebase updates but enables access to modern libraries and performance improvements. Some legacy systems still run Python 2 for compliance reasons with extended vendor support.
Python 3.10
Security Only (until October 2026)Introduced structural pattern matching with match/case syntax. This matters for building parsers, state machines, and complex conditional logic with cleaner code. Many production systems run 3.10 because it balances stability with modern features. If you are starting a new project, consider newer versions unless you have specific library compatibility requirements.
Python 3.11
Security Only (until 2027)Delivered 10 to 60% performance improvements over 3.10 with better error messages. This matters for CPU bound workloads where the speed boost directly reduces compute costs. Exception groups enable better async error handling. Most organizations targeting production stability choose 3.11 as their current standard.
Python 3.12
Current LTS (until October 2028)Introduced per interpreter GIL, enabling better multi threaded performance. Improved f strings with arbitrary expressions. This is the recommended version for new projects balancing feature availability with library ecosystem support. Most major frameworks including Django 5.2 and FastAPI 0.115 fully support 3.12.
Python 3.13
Latest StableExperimental free threaded mode removes the GIL for truly parallel execution. JIT compiler in experimental preview. This matters for the future of Python performance but requires careful evaluation before production adoption. Early adopters are testing these features in non critical workloads.
Technology Fit Assessment
When Python Is the Right Choice (And When It Isn't)
Python is not the right tool for every project. Here is when you should choose Python over alternatives like Go, Node.js, or Java, and when you should not.
CHOOSE PYTHON WHEN
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If your project involves machine learning, deep learning, or data science, choose Python. This applies to: recommendation engines, predictive analytics, natural language processing, computer vision, and any system requiring TensorFlow, PyTorch, or scikit-learn. Python powers over 50% of all AI/ML initiatives globally because the library ecosystem has no equivalent in other languages.
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If time to market matters more than raw performance, choose Python. This applies to: MVPs, prototypes, internal tools, and applications where developer productivity outweighs execution speed. Python code is typically 3 to 5 times shorter than Java equivalent, meaning faster development and easier maintenance.
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If your system processes, transforms, and moves data between systems, choose Python. This applies to: ETL pipelines, data warehousing, analytics platforms, and business intelligence systems. Pandas, Apache Airflow, and PySpark dominate this space for good reason.
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If your project requires integrating with scientific computing, finance, healthcare, or research tools, choose Python. This applies to: quantitative analysis, bioinformatics, academic research, and any domain with specialized libraries. PyPI hosts over 400,000 packages covering virtually every use case.
DO NOT CHOOSE PYTHON WHEN
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Python's interpreted nature makes it slower than compiled languages for CPU bound tasks. Use Go or Rust instead for systems requiring microsecond response times, high frequency trading engines, or performance critical embedded systems where every CPU cycle matters.
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Python is not designed for mobile development. Use Swift for iOS, Kotlin for Android, or React Native/Flutter for cross platform. Python mobile solutions like Kivy exist but lack the native feel and performance that users expect.
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For applications requiring thousands of concurrent WebSocket connections with minimal latency, Node.js handles event driven I/O more efficiently. Use Node.js for chat applications, live collaboration tools, or real time gaming backends.
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JavaScript runs natively in browsers. Python requires compilation to WebAssembly or running server side. Use JavaScript or TypeScript for frontend applications, single page apps, and anything that needs to run in the browser.
Ask yourself: Does this project require AI/ML, data processing, or rapid development more than raw performance? The right choice depends on your specific constraints: performance requirements, team expertise, and long term maintenance considerations. We help you make that decision based on 2000+ projects across multiple languages and frameworks.
"Their Python engineers perform at very high standards. The ML pipeline they built processes 50 million events daily with zero data loss. We have had a strong relationship for almost 4 years."
The best partnerships are the ones you do not have to worry about. They deliver the kind of technical execution and reliability that builds long term trust.
Michael Chen
EVP of Engineering
Built for Technical Leaders
Why Forward-Thinking CTO's Choose HireDeveloper
Our Python developers average 6+ years of production experience, with deep expertise in Django, FastAPI, data processing pipelines, and cloud-native applications. Every developer is vetted for writing clean, efficient, and scalable code not just scripting basics.
We do not hire developers who finished online courses last month. We hire engineers who have shipped production Python in domains where data quality, model accuracy, and system reliability determine success or failure. Every candidate completes a take home assignment building a data pipeline with proper error handling and test coverage, not fizzbuzz. Top 1% acceptance rate.
Your projects ship 40% faster because our developers understand memory leaks in long running processes, GIL limitations for CPU bound work, and async patterns for I/O bound operations before they write code. They profile before optimizing. They benchmark critical paths. They write concurrent code that does not deadlock. No guessing. Every optimization is measured.
We maintain specialists for TensorFlow production deployment, PyTorch research to production pipelines, and data engineering at scale. Developers understand feature stores, model versioning, and ML monitoring. They have deployed recommendation systems handling millions of daily predictions, not tutorial projects. Machine learning veterans, not hobbyists.
Every engagement starts with architecture review. We map your existing system, identify integration points, understand your deployment patterns. Developers join your standups, use your tools, follow your workflows. No parallel universe. Your team expands, not fragments.
ISO 27001 certified. SOC 2 Type II available on request. Zero security incidents in 3 years. 47+ enterprise audits passed. $2M professional liability plus $1M E&O plus cyber insurance coverage. Background checks on every developer: criminal, education, employment verification.
4 to 8 hours overlap with US, EU, or APAC time zones. Core hours availability for standups and code reviews. Async handoffs documented. No black box development. You see commits daily, not monthly.
Dedicated team at monthly rate. Fixed price for defined scope. Hourly for overflow work. Scale up with 1 to 2 weeks notice. Scale down with 2 weeks notice. No long term contracts required.
If a developer does not meet your expectations within the first two weeks, we replace them at no additional cost. No questions asked. We also conduct regular check ins to address concerns before they become problems.
TEAM INTEGRATION
How Our Python Developers Integrate With Your Team
Realistic timeline from first contact to production code
"hello" to code
Discovery
- Requirements call
- Tech stack review
- Team structure mapping
Matching
- Profiles shared
- You interview
- Technical assessment
Onboarding
- Contracts signed
- Access setup
- Tooling configured
Shipping
- First PR merged
- Production code delivered
- Ongoing iteration
How We Use AI in Delivery
AI-POWERED DEVELOPMENT
Faster Shipping, Not Replacement
AI assists our developers at specific decision points. It does not replace their judgment..
Code completion, boilerplate, test scaffolding
Context-aware code, codebase Q&A
API docs, debugging, patterns
For IP-sensitive, local models
How AI Actually Speeds Development
- Documentation generation
- Test case scaffolding
- Boilerplate code completion
- Code explanation and commenting
- Regex and SQL generation
- Repetitive refactoring patterns
- Documentation generation
- Test case scaffolding
- Boilerplate code completion
- Code explanation and commenting
- Regex and SQL generation
- Repetitive refactoring patterns
Real Impact on Your Project
Measured Q4 2024 across 50+ projects
ENTERPRISE SECURITY
Security & IP Protection
Enterprise-grade security for regulated industries
Code ownership assigned to you before repository access granted. Work for hire agreements standard. No retained rights. Your code is your code.
Criminal background check, education verification, employment history validation, reference checks. Every developer, no exceptions. Reports available on request.
Secure office facilities with monitored access. Dedicated devices for client work. USB ports disabled. Screen recording available for compliance sensitive projects.
MFA required for all systems. VPN only access to client infrastructure. 4 hour access revocation guarantee. Role based permissions reviewed monthly.
Full code handover at engagement end. No vendor lock in. Complete documentation transfer. Knowledge transfer sessions included. You walk away with everything.
Python Developers Pricing & Rates
Real Rates, Real Experience
Real rates, real experience levels, no hidden markup
Entry Level
1-3 years experience
Needs supervision
Junior Developers Skills
- Basic Django/Flask
- simple API development
- unit testing
- database queries
Experienced
4-7 years experience
Works independently
Mid-Level Skills
- Django/FastAPI proficiency
- REST API design
- async programming
- CI/CD pipelines
Expert
8+ years experience
Mentors team
Senior Developer Skills
- System design
- ML pipeline development
- performance optimization
- cloud architecture
Architect
10+ years experience
Owns architecture
Lead Developer Skills
- Enterprise architecture
- ML platform design
- team leadership
- technical strategy
We focus on senior+ engineers who ship.
See full pricing breakdownTRANSPARENT PRICING
What's Included in Rate
When we quote "$5,500/month for senior developer," here's exactly what you get:
Dedicated Team
- Predictable monthly cost
- All inclusive (no hidden fees)
- Full time dedicated resource
- Replacement guarantee included
- Management and QA included
$25/hr Freelancer
- Base rate: $4,000/month
- Your time managing: +$2,000 (20 hrs × $100/hr)
- No replacement guarantee (risk cost)
- No vacation/sick coverage (gaps in delivery)
"Our pricing includes the risk mitigation you're actually paying for."
CLIENT CASE STUDIES
Recent Outcomes
Real results from companies that scaled their engineering teams with dedicated development teams.
The Challenge
- Problem: ML models running in notebooks could not handle production transaction volumes
- Risk: Fraud detection latency causing $50K+ daily losses
- Constraint: 8 week deadline before Series B board meeting
Our Approach
- Week 1: Architecture review, identified 4 critical bottlenecks in existing pipeline
- Week 2 to 4: Rebuilt ML serving infrastructure with proper feature stores and model registry
- Week 5 to 8: Deployed real time scoring system with sub 100ms latency guarantee
Verified Outcomes
"They understood our ML challenges immediately. No ramp up time explaining basics. First week they identified issues we had missed for months."
QUICK FIT CHECK
Are We Right For You?
Answer 5 quick questions to see if we're a good match
Question 1 of 5
Is your project at least 3 months long?
Offshore teams need 2-3 weeks to ramp up. Shorter projects lose 25%+ of timeline to onboarding.
FROM OUR EXPERTS
What We're Thinking
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Frequently Asked Questions
Still have questions about hiring Python developers? Explore our FAQs to understand our technical expertise, engagement models, pricing, and how we build scalable, high-performance Python solutions. If you don’t find what you’re looking for, feel free to reach out to us directly we’re happy to help.
How quickly can I hire Python developers through HireDeveloper?
We match you with pre vetted Python developers within 48 hours of receiving your requirements. After you interview and approve candidates (typically 1 to 2 days), developers can start onboarding within 5 days. Most teams have their first production PR merged by Day 12. This assumes you have requirements documented. If you need help defining requirements, add 3 to 5 days for a discovery sprint.
What is your vetting process for Python developers?
Four stage vetting:
(1) Technical assessment covering Python fundamentals plus advanced topics like async programming, memory management, and system design.
(2) Live coding interview with data pipeline or API design component for senior roles.
(3) English communication assessment via video call.
(4) Background verification: criminal, education, employment history. Top 1% of applicants pass.
Average experience of accepted candidates: 7.2 years.
We reject developers who have only tutorial experience, regardless of how well they interview.
Can I interview developers before committing?
Yes, always. We share 2 to 3 candidate profiles with detailed technical backgrounds, project history, and communication samples. You conduct your own interviews however you prefer: technical screens, pair programming, system design. No commitment until you approve. If none fit, we source additional candidates at no cost. You are hiring for your team; we just accelerate the search.
How much does it cost to hire a Python developer?
Monthly rates by experience: Junior (1 to 3 years) $2,500 to $3,500, Mid level (4 to 7 years) $3,500 to $5,000, Senior (8+ years) $5,000 to $7,000, Lead/Architect (10+ years) $7,000 to $10,000+. All rates are fully loaded: compensation, benefits, equipment, infrastructure, management, and replacement insurance. No hidden fees. No setup costs. The rate you see is the rate you pay.
What is included in the monthly rate?
Everything required for the developer to be productive: Base salary and benefits, health insurance, equipment (laptop, monitors, peripherals), software licenses, secure office infrastructure, management overhead, and replacement insurance. You pay one predictable monthly amount. We do not charge for onboarding, knowledge transfer, or reasonable scope clarification calls.
What Python versions and frameworks do your developers work with?
Our Python developers work with Python 3.10 through 3.13, including async/await patterns, type hints, and modern language features. Framework expertise includes Django 5.2 LTS, FastAPI 0.115, and Flask 3.1. For ML/AI: TensorFlow 2.18, PyTorch 2.6, scikit-learn 1.5. Cloud certifications: 68% AWS certified, 45% GCP certified. We match developers to your specific version requirements. If you are on an older version for compliance reasons, we have developers with that experience too.